Apparatus and method for image processing and storage medium

ABSTRACT

An image processing apparatus and method including executing high-pass filtering in a column direction on the pixel values of original image data read from a flat panel detector to obtain first image data, and subtracting a value obtained by converting each of the pixel values of the first image data in accordance with an absolute value of a statistic calculated from pixel values in the same pixel row of the first image data from the value of a corresponding pixel of the original image data to obtain processed image data.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of application Ser. No. 12/403,267,filed on Mar. 12, 2009, which claims priority from Japanese PatentApplication No. 2008-064253 filed on Mar. 13, 2008, which are herebyincorporated by reference herein in their entirety.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a technology for reducing noisegenerated in an image, and in particular, to a technology for reducingnoise generated in each row of an image.

2. Description of the Related Art

A known radiographic apparatus uses a flat panel detector made ofamorphous silicon or polysilicon deposited or formed on a glasssubstrate. Original image data acquired by the flat panel detectorincludes noise components generated in the individual rows of an image,in addition to a pure signal component converted from incident X-rays.The flat panel detector reads the image data as an image signal byturning on and off semiconductor switches present in the same pixel row.In this case, temporal changes in gate signals for the turning on/offare considered to be one of the causes of noise generated in theindividual rows of an image.

A method for reducing linear noise generated in the individual rows ofan image (referred to as “horizontal noise” in Japanese Patent Laid-OpenNo. 2003-204955 (herein after referred to as JP-A-2003-204955)) isdisclosed in JP-A-2003-204955 (FIG. 3 and so on).

The noise reducing technology described in JP-A-2003-204955 extractsnoise components generated in the individual rows of an image byexecuting high-pass filtering in the column direction of an image andlow-pass filtering in the row direction and subtracts the extractednoise components from the original image data.

However, the method described in JP-A-2003-204955 includes not only“horizontal noise” but also signal components that constitute an object.In particular, a region on the image of the object region where thevalues of pixels sharply changes contains the signal component of theobject. Therefore, the “horizontal noise” disclosed in JP-A-2003-204955is influenced by the acquired object image. This may cause the S/N ratioof the image signals to decrease which may be undesired.

To increase the reading speed, in general, a method for dividing a flatpanel detector into multiple regions and reading image signals withdifferent amplifiers for the individual divided regions is adopted.However, this may result in undesired noise quantity differences fromone amplifier to another because of the differences in characteristicamong the amplifiers.

SUMMARY OF THE INVENTION

According to an aspect of the present invention, an image processingapparatus includes a flat panel detector including a reading circuitconfigured to convert X-rays to original image data, a filtering deviceconfigured to execute high-pass filtering in a column direction on pixelvalues of the original image data to obtain first image data, and aprocessing unit configured to subtract a value obtained by convertingthe pixel values of the first image data in accordance with an absolutevalue of a statistic calculated from the pixel values in the same pixelrow of the first image data from the values of corresponding pixels ofthe original image data to obtain processed image data.

Further features of the present invention will become apparent from thefollowing description of exemplary embodiments with reference to theattached drawings, in which like reference characters designate the sameor similar parts throughout the figures thereof.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of the specification, illustrate embodiments of the invention and,together with the description, serve to explain the principles of theinvention.

FIG. 1 is a block diagram of an image processing system according to afirst embodiment of the present invention.

FIG. 2 is a diagram showing the circuit configuration of the imageprocessing apparatus.

FIG. 3 is a flowchart of the process of the first embodiment.

FIG. 4 is a diagram showing an example of filter factors of acolumn-direction filter.

FIG. 5 is a diagram showing an example of filter factors of arow-direction filter.

FIG. 6 is a diagram of a flat panel detector divided into three regionsin the column direction.

FIG. 7 is a diagram showing the relationship between filtered image dataand statistics.

FIGS. 8A-8E are diagrams for describing the effects of the processing.

FIG. 9 is a diagram of a flat panel detector divided into three regionsin the column direction.

DESCRIPTION OF THE EMBODIMENTS

Exemplary embodiments of an apparatus and method for processing imagesaccording to the present invention will now be described in detail inaccordance with the accompanying drawings. The scope of the invention isnot limited to the examples shown in the drawings.

FIG. 1 shows a configuration example of an image processing system 1according to a first embodiment.

An X-ray generating unit 2 exposes an object 4 to X-rays in timing atwhich an exposure switch 3 is pushed. The X-rays that have passedthrough the object 4 are acquired as original image data by a flat paneldetector 5.

The flat panel detector 5 includes a plurality of pixels arrayed in amatrix form. The pixels each have a capacitor that stores an electriccharge that is proportional to the amount of incident X-rays and asemiconductor switch for reading the electric charge from the capacitor.

The flat panel detector 5 includes a reading circuit. The readingcircuit turns on and off the semiconductor switches in sequence for theindividual rows of the matrix pixels. Electric charge taken out byturning on the semiconductor switches are read for the individual rowsof the matrix pixels and converted from analog to digital, and are thustaken as digitized image signals. The values of the image signals areacquired as original image data associated with the positions of theindividual pixels. That is, the original image data includes pixelvalues corresponding to the matrix pixels.

Since the original image data is data in which the positions ofindividual pixels and pixel values are associated with each other, itallows pixel values corresponding to the positions of pixels to beselected therefrom.

The image processing system 1 includes a display 6 for displaying animage based on original image data acquired by the flat panel detector 5and a control unit 7 for controlling the whole of the image processingsystem 1. The control unit 7 includes a CPU 8 and a main memory 9 (notshown).

The CPU 8 controls the operation of the components of the imageprocessing system 1. The main memory 9 stores control programs that theCPU 8 executes and provides working regions during execution of theprograms by the CPU 8. A magnetic disk 10 stores an operating system(OS), device drives for peripherals, various application softwareincluding programs for executing image processing etc., as describedbelow.

FIG. 2 is a block diagram showing the flow of data in an imageprocessing apparatus 11.

A filter circuit 12 serving as a filtering device executes filtering oforiginal image data f(x, y) acquired by the flat panel detector 5 toobtain filtered image data Pf(x, y).

A row direction (also referred to as “the direction of the same pixelrow) in the following description indicates the direction of a pixel rowin which pixels that are selected by the read circuit to read pixelsignals from the pixels lie in a row. A column direction is a directionperpendicular to the pixel rows. For example, as shown in FIG. 6, forpixels arrayed in an M-row by N-column matrix, where the coordinates ofa pixel is (x, y), the direction of coordinates that are selected whenthe value of y is set at a fixed value y_(m) and the value of x ischanged is defined as a row direction. The direction of coordinates thatare selected when the value of x is fixed and the value of y is changedis defined as a vertical direction. A value f(x, y) indicate a pixelvalue corresponding to a pixel at coordinates (x, y).

A statistics calculation circuit 13 serving as a statistics calculatingunit calculates a statistic Dv(x, y) related to noise, to be describedlater, from the filtered image data Pf(x, y). A processing circuit 14serving as a processing unit calculates N1(x, y) indicating the amountof noise, calculated from the first image data Pf(x, y) on the basis ofthe statistic Dv(x, y), as will be described later, and subtracts thevalue N1 (x, y) indicating the amount of noise from the original imagedata f(x, y) to obtain processed image data P(x, y).

Next, referring to the flowchart in FIG. 3, how the control unit 7controls the image processing system 1 will be described. The processshown in FIG. 3 is implemented by the CPU 8 executing a program storedin the main memory 9.

In step S301, the CPU 8 reads the original image data f(x, y) from theflat panel detector 5. Alternatively, the CPU 8 reads original imagedata from a storage unit, for example, a storage medium, such as a FDD,a CD-RW drive, an MO drive, or a ZIP drive, connected to the imageprocessing system 1.

In step S302, the CPU 8 executes the process of inputting the originalimage data f(x, y) to the filter circuit 12. The CPU 8 then controls thefilter circuit 12 to execute high-pass filtering on the original imagedata f(x, y) in the column direction to obtain the first image dataPf(x, y).

FIG. 4 is a diagram showing filter factors of a column-directionhigh-pass filter. FIG. 5 is a diagram showing filter factors of arow-direction low-pass filter. Although the filter factor in thevertical direction and the filter factor in the horizontal direction inFIGS. 4 and 5 are 7, this is by way of example only and not limited tothat particular filter factor. Although the vertical high-pass filterand the horizontal low-pass filter are FIR (finite-duration impulseresponse) filters, they are not limited to that; for example, they maybe IIR (infinite-duration impulse response) filters.

Noise generated in the direction of pixel rows can be extracted byexecuting vertical high-pass filtering because the value changes fromone pixel row to another.

In step S303, the CPU 8 executes the process of inputting the firstimage data Pf(x, y) to the statistics calculation circuit 13, and theCPU 8 controls the statistics calculation circuit 13 so as to obtain thestatistics Dv (x, y) of the individual coordinates (x, y).

In step S304, the CPU 8 controls the processing circuit 14 serving as aprocessing unit to acquire the processed image data P(x, y).

FIG. 6 is a diagram describing acquisition of the processed image dataP(x, y).

Referring to FIG. 6, reference numeral 200 denotes the first image dataPf(x, y), in which assuming that the image size of original image datais M×N, the image size of the first image data Pf(x, y) also becomesM×N. A value Pf(x, y_(m)) indicates the coordinates (x, y_(m)) of thefirst image data Pf(x, y), and reference numeral 201 shows a diagram inwhich only the values in the y_(m) ^(th) row are extracted. Referencenumeral 202 shows the statistic Dv(x, y_(m)) of the y_(m) ^(th) row. Thestatistic Dv(x, y_(m)) is a variance calculated by using five valuesfrom Pf(x−2, y_(m)) to Pf(x+2, y_(m)), as shown in FIG. 6, for example.However, it is not limited to that and may be a statistic, such as amean value or a standard deviation. That is, it may be a variance, amean value, or a standard deviation calculated from the same row and iscalculated from the coordinates of individual pixels on the basis of thefirst image data Pf(x, y) corresponding to the coordinates of pixels ina fixed region. Thus, changes in the value of pixels in the same row canbe expressed as statistics of individual coordinates.

The noise component N1(x, y) of any coordinates (x, y) is calculated bythe following expression [1], for example:

$\begin{matrix}{{N\; 1\left( {x,y} \right)} = \frac{a \cdot {Dm} \cdot {{Pf}\left( {x,y} \right)}}{{{Dv}\left( {x,y} \right)} + {Dm}}} & (1)\end{matrix}$

where a is a coefficient and Dm is an amount of noise of the flat paneldetector 5, which is measured and stored in advance. The amount Dm thatis measured and stored in advance will be described later. The value ais generally about 1. Decreasing a decreases the value N1(x, y) thatindicates a noise component. If a=1 and there is no Dv(x, y), that is,no edge component, Pf(x, y) is subtracted from the original image data.Since Pf(x, y) is a value that is statistically close to Dm, a linearnoise component is subtracted.

The noise component N1(x, y) can be calculated not only by Expression[1] but also by any calculation by which the absolute value of the noisecomponent N1(x, y) decreases when the absolute value of the statisticvalue Dv(x, y_(m)) increases and the absolute value of the noisecomponent N1(x, y) increases when the absolute value of the statisticvalue Dv(x, y_(m)) decreases.

Calculating the decrease in the absolute value of the noise componentN1(x, y) in a region in which changes in pixel value in the same pixelrow are large (for example, corresponding to the edge of the region ofan acquired object) may result in for, example, not breaking the edgecomponent of the object region even if the value N1(x, y) of the noisecomponent is subtracted from the original image data f(x, y). Incontrast, the absolute value of the value N1(x, y) of the noisecomponent increases in a region in which changes in pixel value in thesame pixel row are small.

The first image data Pf(x, y) may be acquired by controlling the filtercircuit 12 to execute high-pass filtering in the vertical direction andlow-pass filtering in the horizontal direction on the original imagedata f(x, y). This can decrease the value of the noise component in thesame pixel row. That is, the levels of noise in the individual columnsare not equal even in the same pixel row.

As described above, FIG. 5 shows an example of filter factors of ahorizontal low-pass filter.

Processed image data P(x, y) is acquired by subtracting the value N1(x,y) of the noise component from the original image data f(x, y).

Next, the value Dm that is measured and stored in advance will bedescribed. The value Dm that is measured and stored in advance is astatistic calculated from original image data that is read from the flatpanel detector 5 without exposure to X-rays. The statistic is a valueobtained by calculating the mean values of the original image data readfrom the flat panel detector 5 without exposure to X-rays for theindividual horizontal rows, calculating the variance of all the meanvalues, and multiplying it by a coefficient.

Accordingly, the mean value Av(y) of a given y^(th) row can becalculated by the following expression [2];

$\begin{matrix}{{{Av}(y)} = {\frac{1}{N}{\sum\limits_{x = 1}^{N}\; {{pd}\left( {x,y} \right)}}}} & (2)\end{matrix}$

where pd(x, y) is the pixel value of an image read from the flat paneldetector 5 without exposure to X-rays.

The statistic Dm that is measured and stored in advance can becalculated by the following expression [3]:

$\begin{matrix}{{Dm} = {\frac{c}{M}{\sum\limits_{y = 1}^{M}\; \left( {{{Av}(y)} - \overset{\_}{Av}} \right)^{2}}}} & (3)\end{matrix}$

where c is a coefficient, and is the mean value of Av(y). Here, Dm isnot limited to a variance but may be a statistic such as a mean value ora standard deviation. The time to measure Dm to be stored can be atfactory shipment or at the installation of the apparatus but may not belimited to those; it may be at any time before reducing the noise.Although the value of c is generally about 3, it is not limited to that.

One of the features is that the value Dm is stored in advance and isused for determination of a noise component, as shown in Expression [1].The value Dm is read as an image signal by the flat panel detector 5 byturning on/off semiconductor switches present in the same pixel row. Inthis case, temporal changes of gate signal for turning on/off areconsidered to be one of the causes of noise generated in the individualrows of an image.

Accordingly, when the value Dm is calculated in advance from linearnoise, with no object present, a pure component caused by the temporalchanges of the gate signals for turning on/off can be extracted. Thisallows effectively extracting only a linear noise component. It is moreeffective to determine the above-described value a by obtaining theamount of X-rays during exposure from the control unit 7 or the X-raygenerating unit 2.

Acquisition of the value N2(x, y) of a second noise component bychanging the value N1(x, y) of the first noise component will now bedescribed. The value N2(x, y) of the second noise component is obtainedby thresholding the value N1(x, y) of the first noise component and iswritten as the following expressions [4]:

if N1(x, y)<b·Dm, then N2(x, y)=b·Dm,

elseif −b·Dm≦Ni(x, y)≦b·Dm, then N2(x, y)=N1(x, y), and

else, then N2(x, y)=b·Dm   [4]

where b is a coefficient.

The value b is generally about 1. Decreasing b decreases the value N2(x,y) of the second noise component.

FIG. 7 shows the relationship between the value N1(x, y) of the firstnoise component and the value N2(x, y) of the second noise component. Asshown in FIG. 7, when the absolute value of the value N1(x, y) of thefirst noise component exceeds a predetermined value, the absolute valueof the value N2(x, y) of the second noise component is limited and doesnot increase. However, the thresholding processing is not limited tothat; it is sufficient that the value N2(x, y) of the second noisecomponent is limited when the absolute value of the value N1(x, y) ofthe first noise component exceeds a predetermined value. For example,the following expressions [5] are possible:

if N1(x, y)<b·Dm, then N2(x, y)=0,

elseif −b·Dm≦Ni(x, y)≦b·Dm, then N2(x, y)=N1(x, y), and

else, then N2(x, y)=0.   [5]

In this case, the value of the second noise component is a constant.

Next, the effects of the processing will be described together with theflow thereof with reference to FIGS. 8A-8E.

An image shown in FIG. 8A of original image data is a graphicalrepresentation of the pixel values of the original image data in which astomach is photographed, which contains linear noise in the same pixelrows. This linear noise appears at random in the rows, and the value ofthe noise component also changes at random.

An image shown in FIG. 8B is a representation of an image of the valueof the filtered image data Pf(x, y). The image shown in FIG. 8B containsa linear noise component and also an image component of the object. Itparticularly contains many horizontal outline components of the object.

In general, when the pixel value of displayed image data is 10 bits, thevalue of a linear noise component is within ±10, but the edge componentof the object in filtered image data is sometimes ±110 or greater.Accordingly, when a filtered image (2) is subtracted from an originalimage (1), the outline of the object is influenced and is degraded inimage quality.

An image shown in FIG. 8C is a graphical representation of the valueN1(x, y) of the first noise component. The value N1(x, y) of the firstnoise component is substantially the same in the same row, whereas thecomponent of the object significantly changes at the outline of theobject. Therefore, the value of the first noise component correspondingto coordinates at which the variance of filtered image data is smallremains, and when the variance of the filtered image data significantlychanges at the outline of the object, local variance increases. Thisdecreases the value of the first noise component. However, the componentof the object still remains in the value of the first noise component.

An image shown in FIG. 8D is a graphical representation of the valueN2(x, y) of the second noise component. As has been described, when thevalue N1(x, y) of the first noise component exceeds a predeterminedvalue, the value N2(x, y) of the second noise component is limited notto increase more. Thus, most of the components of the object areeliminated, but linear noise components remain.

An image shown in FIG. 8E is a representation of the value of processedimage data P(x, y). The second noise component is subtracted from thesignal components of the object. Therefore, degradation of the imagesignal of the object can be reduced in the processed image data P(x, y),thus increasing the effect of reducing the linear noise. This allowsprocessed image data, the S/N ratio of which is improved from theoriginal image data, to be obtained. A second embodiment will now bedescribed.

FIG. 9 is a diagram of the flat panel detector 5 in FIG. 1 divided intothree regions, that is, a region 41, a region 42, and a region 43, inthe column direction to increase the reading speed. The pixels individed region 41 are amplified by an amplifier 61, the pixels individed region 42 are amplified by an amplifier 62, and the pixels individed region 43 are amplified by an amplifier 63. However, the valueof a linear noise component is also amplified at an amplification factorthat differs from one amplifier to another because of differences in theinherent characteristics of the amplifiers. Therefore, the secondembodiment performs noise reduction processing for the individualamplifiers.

To perform noise reduction processing for the individual amplifiers, thevalue N2(x, y) of the second noise component should be obtained for theindividual amplifiers in the flowchart in FIG. 3, described above. Theother processes are the same as described in the first embodiment.

Referring to FIG. 9, the value Nb2(x, y) of the second noise componentin the region 41 is the mean value of the values N1(x, y) that satisfy−c·Dm or greater and c·Dm or less, where c is a coefficient.

This is written as the following expression [6]:

if −c·Dm≦N1(x, y)<c·Dm, then Nb2(x, y)=Avr(N1(x, y))   [6]

where Avr(N1(x, y) indicates the mean value of N1(x, y), and Dm is thevalue that is obtained from the flat panel detector 5 and is measuredand stored in advance, as described in the first embodiment.

The same calculation is performed on the regions 42 and 43 and valuesNb2(x, y) calculated for the individual regions are subtracted from theoriginal image to reduce the noise.

Although the present embodiment is described where the flat paneldetector 5 is divided into three regions, the flat panel detector 5 canbe divided into any number of regions.

The value Dm that is measured and stored in advance may be measured andstored for the individual amplifiers.

Thus, according to the present embodiment, since changes in the valuesof noise components caused by differences in the characteristics ofamplifiers are calculated and corrected for each amplifier, noise can bereduced without degrading an X-ray image.

While the present invention has been described in detail based on theabove-described embodiments, the present invention can also be embodiedas, for example, a system, an apparatus, a method, a program, and astorage medium. Specifically, the present invention may be applied to asystem configured by a plurality of units or an apparatus having oneunit.

The present invention includes a case in which the functions of theabove-described embodiments are implemented by providing a softwareprogram to a system or an apparatus directly or from a remote locationand reading and executing the provided program codes by the computer ofthe system or the apparatus. In this case, the provided program is acomputer program corresponding to the flowchart shown in FIG. 3.

Accordingly, the program codes installed in the computer to implementthe functions of the present invention also achieve the presentinvention. In other words, the present invention includes a computerprogram for implementing the functions of the present invention.

In this case, the present invention may be in the form of an objectcode, a program implemented by an interpreter, or script data providedto an OS that has the functions of the program.

Examples of computer-readable storage media for providing the computerprogram are floppy disks, hard disks, optical disks, magneto-opticaldisks, MOs, CD-ROMs, CD-Rs, CD-RWs, magnetic tape, non-volatile memorycards, ROMs, and DVDs (DVD-ROMs and DVD-Rs).

Another method for providing the program is connecting to a website onthe Internet using a browser of a client computer and downloading thecomputer program of the present invention from the website to arecording medium such as a hard disk. In this case, the programdownloaded may be a compressed file having an automatic installfunction. The program codes that constitute the program of the presentinvention can be implemented by dividing it into more than one files anddownloading the individual files from different websites. In otherwords, the present invention further includes a WWW server through whichprogram files for implementing the functions of the present inventionare downloaded by more than one user.

The present invention may have the form of coding the program of thepresent invention, storing it in a storage medium, such as a CD-ROM, anddistributing it to users. In this case, the present invention may beconfigured to allow a user who satisfies predetermined conditions todownload key information for decoding the code from a website via theInternet, to implement the coded program using the key information, andto allow a computer to install the program.

The computer executes the read program, so that the functions of theforegoing embodiments can be implemented; furthermore, the functions ofthe embodiments may be implemented according to an instruction of theprogram in cooperation with an OS or the like that is operating on thecomputer. In this case, the OS or the like performs part or all of theactual processing, so that the functions of the foregoing embodimentscan be implemented by this processing.

Furthermore, after the program read from the storage medium is writtento a function expansion board inserted into the computer or to a memoryprovided in a function expansion unit connected to the computer, a CPUor the like mounted on the function expansion board or functionexpansion unit performs all or a part of the actual processing so thatthe functions of the foregoing embodiments can be implemented by thisprocessing.

The present invention can prevent noise generated in the individual rowsof an image to reduce degradation of an image signal.

While the present invention has been described with reference toexemplary embodiments, it is to be understood that the invention is notlimited to the disclosed exemplary embodiments. The scope of thefollowing claims is to be accorded the broadest interpretation so as toencompass all modifications and equivalent structures and functions.

1. An image processing apparatus that reduces noise in a row in areading direction from an original image of an object photographed withan x-ray, the image processing apparatus comprising: a first unitconfigured to extract a high-frequency component from the originalimage; a second unit configured to extract an edge component of theobject from the high-frequency component; and a third unit configured toacquire an image in which the noise in a row is reduced from theoriginal image based on the high-frequency component and the edgecomponent.
 2. The image processing apparatus according to claim 1,wherein the third unit acquires an image in which a component, in whichthe edge component is reduced from the high-frequency component, isreduced from the original image.
 3. The image processing apparatusaccording to claim 1, wherein the second unit extracts the edgecomponent of the object based on an evaluation value indicatingmagnitude of a change in a pixel value calculated from a pixel value ina same row in a first image.
 4. The image processing apparatus accordingto claim 3, wherein the evaluation value is any one of a variance value,a mean value and a standard deviation.
 5. The image processing apparatusaccording to claim 1, wherein the third unit sets noise in a row in acorresponding region to zero if the edge component of the object isequal to or greater than a predetermined value.
 6. The image processingapparatus according to claim 1, wherein the second unit sets noise in arow in a corresponding region to a constant if the edge component of theobject is equal to or greater than a predetermined value.
 7. The imageprocessing apparatus according to claim 6, wherein the constant is avalue of a horizontal noise acquired beforehand by a flat panel detectorthat photographs the original image.
 8. The image processing apparatusaccording to claim 3, wherein the flat panel detector that photographsthe original image includes a plurality of amplifiers in each row andthe evaluation value is calculated for each of the amplifiers.
 9. Theimage processing apparatus according to claim 8, wherein a readingcircuit includes an A/D converter configured to convert an image signalinto original image data.
 10. The image processing apparatus accordingto claim 1, wherein the first unit executes high-pass filtering in acolumn direction perpendicular to a row direction in which the originalimage is read.
 11. An image processing method for processing an originalimage in each read row from a flat panel detector including a pluralityof pixels arrayed in a matrix form, the image processing methodcomprising; extracting a high-frequency component including a noisecomponent in a row direction from the original image; and reducing acomponent, in which an edge component of an object is reduced from thehigh-frequency component, from the original image.
 12. Acomputer-readable recording medium storing a program, the programcausing a computer to process a pixel value of an original image in eachrow read from a flat panel detector including a plurality of pixelsarrayed in a matrix form, the program comprising: acquiring a firstimage in which a high-frequency component including a noise component ina row direction is extracted from the original image; acquiring a secondimage in which the edge component of the object is reduced from thefirst image; and reducing the second image from the original image. 13.An image processing apparatus comprising: a flat panel detectorconfigured to convert an x-ray into original image data; a first unitconfigured to execute high-pass filtering on a pixel value of theoriginal image data in a column direction and acquire first image data;a second unit configured to acquire second image data in which a pixelvalue of the first image data is converted into zero if a statisticvalue of a pixel value calculated from a pixel value in a same row in afirst image is larger than a predetermined threshold value; and a thirdunit configured to reduce a pixel value of image data based on thesecond image data from the pixel value of the original image data andacquire processed image data.
 14. An image processing method forprocessing original image data of an object photographed with an x-ray,the image processing method comprising: executing high-pass filtering ona pixel value of the original image data in a column direction andacquiring first image data; acquiring second image data in which a pixelvalue of the first image data is converted into zero if a statisticvalue of a pixel value calculated from a pixel value in a same row in afirst image is larger than a predetermined threshold value; and reducinga pixel value of image data based on the second image data from thepixel value of the original image data and acquiring processed imagedata.
 15. An image processing apparatus that reduces noise in a row froman original image of an object photographed with an x-ray, the imageprocessing apparatus comprising: a first unit configured to extract ahigh-frequency component from the original image; and a second unitconfigured to extract an edge component of the object from thehigh-frequency component.